一种改进的自适应阈值RANSAC中耕作物行检测方法

2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI:10.1109/ICSP51882.2021.9408744

Y. Xie, Kai Chen, Wentao Li, Yan Zhang, J. Mo

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引用次数: 1

摘要

针对单帧图像中由于生长条件不同而出现叶片不规则的作物行，提出了一种结合垂直投影和自适应阈值RANSAC的直线提取方法。首先，通过垂直投影图获取主干层段;然后，根据不同作物行各自的垂直投影得到拟合参数。最后，对滤波后得到的不同作物行拟合区间进行自适应阈值RANSAC拟合。实验表明，该算法对单帧图像的检测率为96.76%，准确率为92.18%，均高于其他算法。平均检测拟合时间为243ms，可满足农机实时性检测要求。

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An Improved Adaptive Threshold RANSAC Method for Medium Tillage Crop Rows Detection

In view of crop rows with irregular leaves due to different growth conditions in a single frame image, A line extraction method combining vertical projection and adaptive threshold RANSAC is proposed. Firstly, the backbone intervals are obtained through vertical projection map. Then, fitting parameters of different crop rows are obtained from their respective vertical projections. Finally, the adaptive threshold RANSAC fitting is performed in different crop rows fitting intervals obtained by filtered. Experiments show that the detection rate of single frame image is 96.76%, and the accuracy is 92.18%, which are higher than other algorithms. The average detection fitting time is 243ms, which can meet the real-time detection requirements of agricultural machinery.

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2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)

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